Social robot capabilities, such as talking gestures, are best produced using data driven approaches to avoid being repetitive and to show trustworthiness. However, there is a lack of robust quantitative methods that allow to compare such methods beyond visual evaluation. In this paper a quantitative analysis is performed that compares two Generative Adversarial Networks based gesture generation approaches. The aim is to measure characteristics such as fidelity to the original training data, but at the same time keep track of the degree of originality of the produced gestures. Principal Coordinate Analysis and procrustes statistics are performed and a new Fréchet Gesture Distance is proposed by adapting the Fréchet Inception Distance to gestures. These three techniques are taken together to asses the fidelity/originality of the generated gestures.

社交机器人功能（例如说话手势）最好使用数据驱动的方法来产生，以避免重复并表现出可信赖性。但是，缺乏可靠的定量方法，无法对这些方法进行视觉评估以外的比较。在本文中进行了定量分析，该分析比较了两种基于对抗网络的手势生成方法。目的是测量诸如对原始训练数据的保​​真度的特征，但是同时跟踪所产生的手势的原创性程度。进行主坐标分析和过程统计，并通过将Fréchet起始距离适应手势来提出新的Fréchet手势距离。结合使用这三种技术来评估所生成手势的保真度/原始性。